Feature selection with Intelligent Dynamic Swarm and Rough Set

Authors:
Changseok Bae;Wei-Chang Yeh;Yuk Ying Chung;Sin-Long Liu
Affiliations:
Personal Computing Research Team, ETRI, 161 Gajeong Dong, Yuseong Gu, Daejeon, Korea;e-Integration & Collaboration Laboratory, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan, ROC;School of Information Technologies, University of Sydney, NSW 2006, Australia;e-Integration & Collaboration Laboratory, Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu 30013, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Data mining is the most commonly used name to solve problems by analyzing data already present in databases. Feature selection is an important problem in the emerging field of data mining which is aimed at finding a small set of rules from the training data set with predetermined targets. Many approaches, methods and goals including Genetic Algorithms (GA) and swarm-based approaches have been tried out for feature selection in order to these goals. Furthermore, a new technique which named Particle Swarm Optimization (PSO) has been proved to be competitive with GA in several tasks, mainly in optimization areas. However, there are some shortcomings in PSO such as premature convergence. To overcome these, we propose a new evolutionary algorithm called Intelligent Dynamic Swarm (IDS) that is a modified Particle Swarm Optimization. Experimental results states competitive performance of IDS. Due to less computing for swarm generation, averagely IDS is over 30% faster than traditional PSO.